The acclimative biogeochemical model of the southern North Sea

Abstract

Abstract. Ecosystem models often rely on heuristic descriptions of autotrophic growth that fail to reproduce various stationary and dynamic states of phytoplankton cellular composition observed in laboratory experiments. Here, we present the integration of an advanced phytoplankton growth model within a coupled three-dimensional physicalbiogeochemical model and the application of the model system to the southern North Sea (SNS) defined on a relatively high resolution ( ∼ 1.5-4.5 km) curvilinear grid. The autotrophic growth model, recently introduced by Wirtz and Kerimoglu (2016), is based on a set of novel concepts for the allocation of internal resources and operation of cellular metabolism. The coupled model system consists of the General Estuarine Transport Model (GETM) as the hydrodynamical driver, a lower-trophic-level model and a simple sediment diagenesis model. We force the model system with realistic atmospheric and riverine fluxes, background turbidity caused by suspended particulate matter (SPM) and open ocean boundary conditions. For a simulation for the period 2000-2010, we show that the model system satisfactorily reproduces the physical and biogeochemical states of the system within the German Bight characterized by steep salinity; nutrient and chlorophyll (Chl) gradients, as inferred from comparisons against observation data from long-term monitoring stations; sparse in situ measurements; continuous transects; and satellites. The model also displays skill in capturing the formation of thin chlorophyll layers at the pycnocline, which is frequently observed within the stratified regions during summer. A sensitivity analysis reveals that the vertical distributions of phytoplankton concentrations estimated by the model can be qualitatively sensitive to the description of the light climate and dependence of sinking rates on the internal nutrient reserves. A non-acclimative (fixed-physiology) version of the model predicted entirely different vertical profiles, suggesting that accounting for physiological flexibility might be relevant for a consistent representation of the vertical distribution of phytoplankton biomass. Our results point to significant variability in the cellular chlorophyll-to-carbon ratio (Chl : C) across seasons and the coastal to offshore transition. Up to 3-fold-higher Chl : C at the coastal areas in comparison to those at the offshore areas contribute to the steepness of the chlorophyll gradient. The model also predicts much higher phytoplankton concentrations at the coastal areas in comparison to its non-acclimative equivalent. Hence, findings of this study provide evidence for the relevance of physiological flexibility, here reflected by spatial and seasonal variations in Chl : C, for a realistic description of biogeochemical fluxes, particularly in the environments displaying strong resource gradients.

The large-scale impact of offshore wind farm structures on pelagic primary productivity in the southern North Sea

“…4). Simulated Chl-a concentrations in this area are also lower than those estimated by the satellite imagery (e.g., Kerimoglu et al 2017;Ford et al 2017), while in the open SNS, our model modestly overestimates Chl-a and probably also NPP. NPP simulated by van Leeuwen et al (2013) with the same hydrodynamic (GETM) but with a different ecosystem model (ERSEM, Baretta et al 1995) is much higher (on average 318 ± 29 g m −2 a −1 ) than NPP simulated here for their region termed "SNS", referring to a small area of the Southern Bight of the North Sea.…”

“…Our coupled setup differs in two respects: (i) we resolve filtration (see Sect. 2.5.1), and (ii) we used the full 3D biogeochemical model OmexDia based on Soetaert et al (1996) instead of the the single layer soil parameterization by Kerimoglu et al (2017). There, top-down mortality of zooplankton is uniform, while we prescribe higher zooplankton mortality near the coast.…”

“…For the integrated modelling of benthic and epistructural filtration, water physics and pelagic biogeochemistry, we use the recently introduced modular framework by Lemmen et al (2018), which contains a novel ecosystem model recently applied to and verified for the SNS by Kerimoglu et al (2017). Multi-annual simulations run with and without epistructural mussels allow a first estimate of the sensitivity of pelagic primary productivity to the projected OWFs in this regional sea.…”

A model-based projection of historical state of a coastal ecosystem: Relevance of phytoplankton stoichiometry

“…For the construction of the recent (or control; C ) and historic (H ) states of the SNS, we used the coupled physical-biogeochemical model system recently introduced by Kerimoglu et al (2017). Dynamics of the pelagic biogeochemistry is described by the Model for Adaptive Ecosystems in Coastal Seas (MAECS), which, otherwise similar to other ecosystem models with respect to the recycling of the organic and inorganic macro-nutrients (carbon (C), N and P), includes an optimality based physiological acclimation model of phytoplankton growth (Wirtz & Kerimoglu, 2016).…”

A 1-Dimensional Sympagic–Pelagic–Benthic Transport Model (SPBM): Coupled Simulation of Ice, Water Column, and Sediment Biogeochemistry, Suitable for Arctic Applications

“…The FABM coupling allows the user to construct their own biogeochemical model using existing modules in the FABM library plus any new modules written by the user (SPBM does not itself provide any new biogeochemical modules). The FABM library is rapidly expanding and presently includes modules from some of the most detailed published biogeochemical models, e.g., The European regional seas ecosystem model (ERSEM) [21], the bottom-redox model biogeochemistry (BROM-biogeochemistry) [22], the PCLake aquatic ecosystem model [23], and the model for adaptive ecosystems in coastal seas (MAECS) [24,25]. As with FABM, SPBM transport code is written in FORTRAN.…”